1
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Castro DC, Chan-Andersen P, Romanova EV, Sweedler JV. Probe-based mass spectrometry approaches for single-cell and single-organelle measurements. MASS SPECTROMETRY REVIEWS 2024; 43:888-912. [PMID: 37010120 PMCID: PMC10545815 DOI: 10.1002/mas.21841] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/09/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
Exploring the chemical content of individual cells not only reveals underlying cell-to-cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single-cell and single-organelle chemical characterization. As the chemical coverage and throughput of single-cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix-assisted laser desorption/ionization MS approaches for single-cell and single-organelle characterization, which is followed by advances in mass spectral data visualization and analysis.
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Affiliation(s)
- Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Peter Chan-Andersen
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Elena V. Romanova
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Jonathan V. Sweedler
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL USA
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2
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Su P, Hollas MAR, Butun FA, Kanchustambham VL, Rubakhin S, Ramani N, Greer JB, Early BP, Fellers RT, Caldwell MA, Sweedler JV, Kafader JO, Kelleher NL. Single Cell Analysis of Proteoforms. J Proteome Res 2024; 23:1883-1893. [PMID: 38497708 DOI: 10.1021/acs.jproteome.4c00075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
We introduce single cell Proteoform imaging Mass Spectrometry (scPiMS), which realizes the benefit of direct solvent extraction and MS detection of intact proteins from single cells dropcast onto glass slides. Sampling and detection of whole proteoforms by individual ion mass spectrometry enable a scalable approach to single cell proteomics. This new scPiMS platform addresses the throughput bottleneck in single cell proteomics and boosts the cell processing rate by several fold while accessing protein composition with higher coverage.
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Affiliation(s)
- Pei Su
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Michael A R Hollas
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Fatma Ayaloglu Butun
- Proteomics Center of Excellence, Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
| | - Vijaya Lakshmi Kanchustambham
- Proteomics Center of Excellence, Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
| | - Stanislav Rubakhin
- Beckman Institute and Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Namrata Ramani
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Joseph B Greer
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Bryan P Early
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Ryan T Fellers
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Michael A Caldwell
- Proteomics Center of Excellence, Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
| | - Jonathan V Sweedler
- Beckman Institute and Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Jared O Kafader
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
| | - Neil L Kelleher
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, United States
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3
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Xie YR, Castro DC, Rubakhin SS, Trinklein TJ, Sweedler JV, Lam F. Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry. Nat Methods 2024; 21:521-530. [PMID: 38366241 PMCID: PMC10927565 DOI: 10.1038/s41592-024-02171-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 01/08/2024] [Indexed: 02/18/2024]
Abstract
Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Daniel C Castro
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Stanislav S Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Timothy J Trinklein
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Jonathan V Sweedler
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
| | - Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
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4
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Croslow SW, Trinklein TJ, Sweedler JV. Advances in multimodal mass spectrometry for single-cell analysis and imaging enhancement. FEBS Lett 2024; 598:591-601. [PMID: 38243373 PMCID: PMC10963143 DOI: 10.1002/1873-3468.14798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 12/11/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024]
Abstract
Multimodal mass spectrometry (MMS) incorporates an imaging modality with probe-based mass spectrometry (MS) to enable precise, targeted data acquisition and provide additional biological and chemical data not available by MS alone. Two categories of MMS are covered; in the first, an imaging modality guides the MS probe to target individual cells and to reduce acquisition time by automatically defining regions of interest. In the second category, imaging and MS data are coupled in the data analysis pipeline to increase the effective spatial resolution using a higher resolution imaging method, correct for tissue deformation, and incorporate fine morphological features in an MS imaging dataset. Recent methodological and computational developments are covered along with their application to single-cell and imaging analyses.
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Affiliation(s)
- Seth W. Croslow
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Timothy J. Trinklein
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Jonathan V. Sweedler
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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5
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Xie YR, Castro DC, Rubakhin SS, Trinklein TJ, Sweedler JV, Lam F. Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.31.543144. [PMID: 37398021 PMCID: PMC10312594 DOI: 10.1101/2023.05.31.543144] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Elucidating the spatial-biochemical organization of the brain across different scales produces invaluable insight into the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive chemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping via MEISTER, an integrative experimental and computational mass spectrometry framework. MEISTER integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating 3D molecular distributions, and a data integration method fitting cell-specific mass spectra to 3D data sets. We imaged detailed lipid profiles in tissues with data sets containing millions of pixels, and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents, and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future developments of multiscale technologies for biochemical characterization of the brain.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Daniel C. Castro
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Stanislav S. Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Timothy J. Trinklein
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Jonathan V. Sweedler
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carl R. Woese Institute for Genomic Biology. University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carl R. Woese Institute for Genomic Biology. University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
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6
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Meng X, Xu P, Tao F. RespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle. iScience 2023; 26:107069. [PMID: 37426353 PMCID: PMC10329182 DOI: 10.1016/j.isci.2023.107069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/18/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023] Open
Abstract
Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing, and agriculture problems. However, the DBTL cycle's Learn (L) step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing data and chaotic metabolic networks. Herein, we develop a method, "RespectM," based on mass spectrometry imaging, which is able to detect metabolites at a rate of 500 cells per hour with high efficiency. In this study, 4,321 single cell level metabolomics data were acquired, representing metabolic heterogeneity. An optimizable deep neural network was applied to learn from metabolic heterogeneity and a "heterogeneity-powered learning (HPL)" based model was trained as well. By testing the HPL based model, we suggest minimal operations to achieve high triglyceride production for engineering. The HPL strategy could revolutionize rational design and reshape the DBTL cycle.
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Affiliation(s)
- Xuanlin Meng
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Ping Xu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Fei Tao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
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7
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Sun S, Tang W, Li B. Authentication of Single Herbal Powders Enabled by Microscopy-Guided In Situ Auto-sampling Combined with Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry. Anal Chem 2023; 95:7512-7518. [PMID: 37134260 DOI: 10.1021/acs.analchem.2c05517] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
In the long history of investigation of herbal products, microscopic examination has greatly contributed to the authentication of herbs in a powder form. However, it cannot provide the chemical profiles of herbal powders and thus is limited to morphological identification. In this work, we present a label-free and automatic approach for the characterization and identification of single herbal powders and their adulterants, enabled through the combination of microscopy-guided auto-sampling and matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS). To meet the demand for automatic and highly efficient in situ extraction, the glass slide was coated with gelatin to immobilize dried herbal powders that cannot stick to the glass slide like fresh and hydrated cells. The gelatin coating also facilitated the pump-out of chemical components and prevented diffusion across the interface enabled by the formation of a tight contact at the probe tip and surface. Optical microscopy was applied to acquire the microstructure and position of the herbal powders immobilized on the gelatin-coated slide. The candidate single herbal powders were picked out by a software for subsequent auto-sampling and MALDI MS identification. The combination of microstructure features and chemical profiles significantly improved the authentication capability of microscopic examination.
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Affiliation(s)
- Shipeng Sun
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Weiwei Tang
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Bin Li
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
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8
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Castro DC, Smith KW, Norsworthy MD, Rubakhin SS, Weisbrod CR, Hendrickson CL, Sweedler JV. Single-Cell and Subcellular Analysis Using Ultrahigh Resolution 21 T MALDI FTICR Mass Spectrometry. Anal Chem 2023; 95:6980-6988. [PMID: 37070980 PMCID: PMC10190686 DOI: 10.1021/acs.analchem.3c00393] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
The mammalian brain contains ∼20,000 distinct lipid species that contribute to its structural organization and function. The lipid profiles of cells change in response to a variety of cellular signals and environmental conditions that result in modulation of cell function through alteration of phenotype. The limited sample material combined with the vast chemical diversity of lipids makes comprehensive lipid profiling of individual cells challenging. Here, we leverage the resolving power of a 21 T Fourier-transform ion cyclotron resonance (FTICR) mass spectrometer for chemical characterization of individual hippocampal cells at ultrahigh mass resolution. The accuracy of the acquired data allowed differentiation of freshly isolated and cultured hippocampal cell populations, as well as finding differences in lipids between the soma and neuronal processes of the same cell. Differences in lipids include TG 42:2 observed solely in the cell bodies and SM 34:1;O2 found only in the cellular processes. The work represents the first mammalian single cells analyzed at ultrahigh resolution and is an advance in the performance of mass spectrometry (MS) for single-cell research.
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Affiliation(s)
- Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, 505 South Goodwin Avenue, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801, United States
| | - Karl W. Smith
- National High Magnetic Field Laboratory, Florida State University, 1801 East Paul Dirac Drive, Tallahassee, FL 32310, United States
| | - Miles D. Norsworthy
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801, United States
- Department of Cell and Developmental Biology, University of Illinois at Urbana-Champaign, 505 South Goodwin Avenue, Urbana, IL 61801 United States
| | - Stanislav S. Rubakhin
- Department of Chemistry, University of Illinois at Urbana-Champaign, 505 South Mathews Avenue, Urbana IL 61801, United States
- Neuroscience Program, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801, United States
| | - Chad R. Weisbrod
- National High Magnetic Field Laboratory, Florida State University, 1801 East Paul Dirac Drive, Tallahassee, FL 32310, United States
| | - Christopher L. Hendrickson
- National High Magnetic Field Laboratory, Florida State University, 1801 East Paul Dirac Drive, Tallahassee, FL 32310, United States
| | - Jonathan V. Sweedler
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, 505 South Goodwin Avenue, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois at Urbana-Champaign, 505 South Mathews Avenue, Urbana IL 61801, United States
- Neuroscience Program, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801, United States
- Department of Bioengineering, University of Illinois at Urbana-Champaign, 1406 West Green Street, Urbana, IL 61801, United States
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9
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Mogilenko DA, Sergushichev A, Artyomov MN. Systems Immunology Approaches to Metabolism. Annu Rev Immunol 2023; 41:317-342. [PMID: 37126419 DOI: 10.1146/annurev-immunol-101220-031513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.
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Affiliation(s)
- Denis A Mogilenko
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Current affiliation: Department of Medicine, Department of Pathology, Microbiology, and Immunology, and Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Alexey Sergushichev
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia
| | - Maxim N Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
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10
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Lee DK, Rubakhin SS, Sweedler JV. Chemical Decrosslinking-Based Peptide Characterization of Formaldehyde-Fixed Rat Pancreas Using Fluorescence-Guided Single-Cell Mass Spectrometry. Anal Chem 2023; 95:6732-6739. [PMID: 37040477 DOI: 10.1021/acs.analchem.3c00612] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Approaches for the characterization of proteins/peptides in single cells of formaldehyde-fixed (FF) tissues via mass spectrometry (MS) are still under development. The lack of a general method for selectively eliminating formaldehyde-induced crosslinking is a major challenge. A workflow is shown for the high-throughput peptide profiling of single cells isolated from FF tissues, here the rodent pancreas, which possesses multiple peptide hormones from the islets of Langerhans. The heat treatment is enhanced by a collagen-selective multistep thermal process assisting efficient isolation of islets from the FF pancreas and, subsequently, their dissociation into single islet cells. Hydroxylamine-based chemical decrosslinking helped restore intact peptide signals from individual isolated cells. Subsequently, an acetone/glycerol-assisted cell dispersion was optimized for spatially resolved cell deposition onto glass slides, while a glycerol solution maintained the hydrated state of the cells. This sample preparation procedure allowed peptide profiling in FF single cells by fluorescence-guided matrix-assisted laser desorption ionization MS. Here, 2594 single islet cells were analyzed and 28 peptides were detected, including insulin C-peptides and glucagon. T-distributed stochastic neighbor embedding (t-SNE) data visualization demonstrated that cells cluster based on cell-specific pancreatic peptide hormones. This workflow expands the sample availability for single-cell MS characterization to a wide range of formaldehyde-treated tissue specimens stored in biobanks.
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Affiliation(s)
- Dong-Kyu Lee
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Stanislav S Rubakhin
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Jonathan V Sweedler
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
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11
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Zhou S, Fatma Z, Xue P, Mishra S, Cao M, Zhao H, Sweedler JV. Mass Spectrometry-Based High-Throughput Quantification of Bioproducts in Liquid Culture. Anal Chem 2023; 95:4067-4076. [PMID: 36790390 DOI: 10.1021/acs.analchem.2c04845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
To meet the ever-increasing need for high-throughput screening in metabolic engineering, information-rich, fast screening methods are needed. Mass spectrometry (MS) provides an efficient and general approach for metabolite screening and offers the capability of characterizing a broad range of analytes in a label-free manner, but often requires a range of sample clean-up and extraction steps. Liquid extraction surface analysis (LESA) coupled MS is an image-guided MS surface analysis approach that directly samples and introduces metabolites from a surface to MS. Here, we combined the advantages of LESA-MS and an acoustic liquid handler with stable isotope-labeled internal standards. This approach provides absolute quantitation of target chemicals from liquid culture-dried droplets and enables high-throughput quantitative screening for microbial metabolites. In this study, LESA-MS was successfully applied to quantify several different metabolites (itaconic acid, triacetic acid lactone, and palmitic acid) from different yeast strains in different mediums, demonstrating its versatility, accuracy, and efficiency across a range of microbial engineering applications.
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Affiliation(s)
- Shuaizhen Zhou
- Department of Energy Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Energy Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Energy Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shekhar Mishra
- Department of Energy Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Mingfeng Cao
- Department of Energy Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Energy Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Jonathan V Sweedler
- Department of Energy Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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12
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Xie YR, Chari VK, Castro DC, Grant R, Rubakhin SS, Sweedler JV. Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry. J Proteome Res 2023; 22:491-500. [PMID: 36695570 PMCID: PMC9901547 DOI: 10.1021/acs.jproteome.2c00714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states of individual cells. Image-guided single-cell mass spectrometry leverages optical and fluorescence microscopy in the high-throughput analysis of cellular and subcellular targets. In this work, we propose DATSIGMA (DAta-driven Tools for Single-cell analysis using Image-Guided MAss spectrometry), a workflow based on data-driven and machine learning approaches for feature extraction and enhanced interpretability of complex single-cell mass spectrometry data. Here, we implemented our toolset with user-friendly programs and tested it on multiple experimental data sets that cover a wide range of biological applications, including classifying various brain cell types. Because it is open-source, it offers a high level of customization and can be easily adapted to other types of single-cell mass spectrometry data.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Varsha K. Chari
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Romans Grant
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Stanislav S. Rubakhin
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Jonathan V. Sweedler
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Mailing Address: Department of Chemistry, University of Illinois, 71 RAL, Box 63-5, 600 South Mathews Avenue, Urbana, Illinois 61801, United States; Phone: (217) 244-7359;
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13
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Saller KM, Pernusch DC, Schwarzinger C. MALINTO: A New MALDI Interpretation Tool for Enhanced Peak Assignment and Semiquantitative Studies of Complex Synthetic Polymers. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:293-303. [PMID: 36599090 PMCID: PMC9896554 DOI: 10.1021/jasms.2c00311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/15/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Abstract
The newly developed MALDI interpretation tool ("MALINTO") allows for the accelerated characterization of complex synthetic polymers via MALDI mass spectrometry. While existing software provides solutions for simple polymers like poly(ethylene glycol), polystyrene, etc., they are limited in their application on polycondensates synthesized from two different kinds of monomers (e.g., diacid and diol in polyesters). In addition to such A2 + B2 polycondensates, MALINTO covers branched and even multicyclic polymer systems. Since the MALINTO software works based on input data of monomers/repeating units, end groups, and adducts, it can be applied on polymers whose components are previously known or elucidated. Using these input data, a list with theoretically possible polymer compositions and resulting m/z values is calculated, which is further compared to experimental mass spectrometry data. For optional semiquantitative studies, peak areas are allocated according to their assigned polymer composition to evaluate both comonomer and terminating group ratios. Several tools are implemented to avoid mistakes, for example, during peak assignment. In the present publication, the functions of MALINTO are described in detail and its broad applicability on different linear polymers as well as branched and multicyclic polycondensates is demonstrated. Fellow researchers will benefit from the accelerated peak assignment using the freely available MALINTO software and might be encouraged to explore the potential of MALDI mass spectrometry for (semi)quantitative applications.
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Affiliation(s)
- Klara M. Saller
- Institute
for Chemical Technology of Organic Materials, Johannes Kepler University Linz, Altenbergerstrasse 69, 4040Linz, Austria
| | - Daniel C. Pernusch
- Institute
for Chemical Technology of Organic Materials, Johannes Kepler University Linz, Altenbergerstrasse 69, 4040Linz, Austria
| | - Clemens Schwarzinger
- Institute
for Chemical Technology of Organic Materials, Johannes Kepler University Linz, Altenbergerstrasse 69, 4040Linz, Austria
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14
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Antevska A, Long CC, Dupuy SD, Collier JJ, Karlstad MD, Do TD. Mouse Pancreatic Peptide Hormones Probed at the Sub-Single-Islet Level: The Effects of Acute Corticosterone Treatment. J Proteome Res 2023; 22:235-245. [PMID: 36412564 DOI: 10.1021/acs.jproteome.2c00668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
We combine liquid chromatography coupled with ion mobility spectrometry-mass spectrometry to elucidate how short exposure to corticosterone (Cort) alters the output of mouse pancreatic islet hormones. The workflow enables the robust separation of mouse insulin 1 (Ins1) and insulin 2 (Ins2) and the detection of major islet hormones in a homogenate equivalent to 100-150 islet cells. We show that Ins2 has a unique structure and is degraded much faster than Ins1. Further investigation indicates that Ins2 may populate both T and R states, whereas Ins1 may not. The assemblies of Ins1's B-chain also introduce more structural heterogeneity than Ins2. Collectively, these features account for their unique degradation profiles, the diabetes risk associated with Ins1, and the protective effect of Ins2. In the same experiments, we observe that the ratio of amylin to Ins1 increased significantly in Cort-treated mice (15:1) compared to the control mice (42:1), correlating well with β-cell proliferation observed in immunoassays on the same animal model. We observe no increase in intact full-length insulin levels but more of the truncated forms, indicating that enzymatic activity is accelerated. Our data provide a molecular basis for reduced insulin action induced by Cort and connections between insulin turnover and insulin resistance.
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Affiliation(s)
- Aleksandra Antevska
- Department of Chemistry, University of Tennessee, Knoxville, Tennessee37996, United States
| | - Connor C Long
- Department of Biochemistry, Cellular, and Molecular Biology, University of Tennessee, Knoxville, Tennessee37996, United States
| | - Samuel D Dupuy
- Department of Surgery, Graduate School of Medicine, University of Tennessee, Knoxville, Tennessee37996, United States
| | - J Jason Collier
- Laboratory of Islet Biology and Inflammation, Pennington Biomedical Research Center, Baton Rouge, Louisiana70808, United States
| | - Michael D Karlstad
- Department of Surgery, Graduate School of Medicine, University of Tennessee, Knoxville, Tennessee37996, United States
| | - Thanh D Do
- Department of Chemistry, University of Tennessee, Knoxville, Tennessee37996, United States
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15
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Zhang Z, Bao C, Jiang L, Wang S, Wang K, Lu C, Fang H. When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective. Front Oncol 2023; 12:1054233. [PMID: 36686803 PMCID: PMC9854130 DOI: 10.3389/fonc.2022.1054233] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/20/2022] [Indexed: 01/07/2023] Open
Abstract
Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.
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Affiliation(s)
- Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Jiang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kankan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,*Correspondence: Hai Fang,
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16
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Hu R, Li Y, Yang Y, Liu M. Mass spectrometry-based strategies for single-cell metabolomics. MASS SPECTROMETRY REVIEWS 2023; 42:67-94. [PMID: 34028064 DOI: 10.1002/mas.21704] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 05/05/2021] [Accepted: 05/11/2021] [Indexed: 06/12/2023]
Abstract
Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.
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Affiliation(s)
- Rui Hu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ying Li
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yunhuang Yang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Maili Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
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17
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Ali A, Davidson S, Fraenkel E, Gilmore I, Hankemeier T, Kirwan JA, Lane AN, Lanekoff I, Larion M, McCall LI, Murphy M, Sweedler JV, Zhu C. Single cell metabolism: current and future trends. Metabolomics 2022; 18:77. [PMID: 36181583 PMCID: PMC10063251 DOI: 10.1007/s11306-022-01934-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022]
Abstract
Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.
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Affiliation(s)
- Ahmed Ali
- Leiden Academic Centre for Drug Research, University of Leiden, Gorlaeus Building Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Shawn Davidson
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Ernest Fraenkel
- Department of Biological Engineering and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ian Gilmore
- National Physical Laboratory, Teddington, TW11 0LW, Middlesex, UK
| | - Thomas Hankemeier
- Leiden Academic Centre for Drug Research, University of Leiden, Room number GW4.07, Gorlaeus Building, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Jennifer A Kirwan
- Berlin Institute of Health, Metabolomics Platform, Translational Research Unit of the Charite-Universitätsmedizin Berlin, Anna-Louisa-Karsch-Str 2, 10178, Berlin, Germany
| | - Andrew N Lane
- Department of Toxicology and Cancer Biology, and Center for Environmental and Systems Biochemistry, University of Kentucky, 789 S. Limestone St, Lexington, KY, 40536, USA.
| | - Ingela Lanekoff
- Department of Chemistry-BMC, Uppsala University, Husargatan 3 (576), 751 23, Uppsala, Sweden
| | - Mioara Larion
- Center for Cancer Research, National Cancer Institute, Building 37, Room 1136A, Bethesda, MD, 20892, USA
| | - Laura-Isobel McCall
- Department of Chemistry & Biochemistry, Department of Microbiology and Plant Biology, Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, 101 Stephenson Parkway, room 3750, Norman, OK, 73019-5251, USA
| | - Michael Murphy
- Departments of Biological Engineering, Department of Electrical Engineering, and Computer Science and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, USA
| | - Jonathan V Sweedler
- Department of Chemistry, and the Beckman Institute, University of Illinois Urbana-Champaign, 505 South Mathews Avenue, Urbana, IL, 61801, USA
| | - Caigang Zhu
- Department of Biomedical Engineering, University of Kentucky, Lexington, KY, 40536, USA
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18
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Bien T, Koerfer K, Schwenzfeier J, Dreisewerd K, Soltwisch J. Mass spectrometry imaging to explore molecular heterogeneity in cell culture. Proc Natl Acad Sci U S A 2022; 119:e2114365119. [PMID: 35858333 PMCID: PMC9303856 DOI: 10.1073/pnas.2114365119] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 05/13/2022] [Indexed: 01/13/2023] Open
Abstract
Molecular analysis on the single-cell level represents a rapidly growing field in the life sciences. While bulk analysis from a pool of cells provides a general molecular profile, it is blind to heterogeneities between individual cells. This heterogeneity, however, is an inherent property of every cell population. Its analysis is fundamental to understanding the development, function, and role of specific cells of the same genotype that display different phenotypical properties. Single-cell mass spectrometry (MS) aims to provide broad molecular information for a significantly large number of cells to help decipher cellular heterogeneity using statistical analysis. Here, we present a sensitive approach to single-cell MS based on high-resolution MALDI-2-MS imaging in combination with MALDI-compatible staining and use of optical microscopy. Our approach allowed analyzing large amounts of unperturbed cells directly from the growth chamber. Confident coregistration of both modalities enabled a reliable compilation of single-cell mass spectra and a straightforward inclusion of optical as well as mass spectrometric features in the interpretation of data. The resulting multimodal datasets permit the use of various statistical methods like machine learning-driven classification and multivariate analysis based on molecular profile and establish a direct connection of MS data with microscopy information of individual cells. Displaying data in the form of histograms for individual signal intensities helps to investigate heterogeneous expression of specific lipids within the cell culture and to identify subpopulations intuitively. Ultimately, t-MALDI-2-MSI measurements at 2-µm pixel sizes deliver a glimpse of intracellular lipid distributions and reveal molecular profiles for subcellular domains.
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Affiliation(s)
- Tanja Bien
- Institute of Hygiene, University of Münster, 48149 Münster, Germany
- Interdisciplinary Center for Clinical Research (IZKF), University of Münster, 48149 Münster, Germany
| | - Krischan Koerfer
- Institute for Psychology, University of Münster, 48149 Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioural Neuroscience, University of Münster, 48149 Münster, Germany
| | - Jan Schwenzfeier
- Institute of Hygiene, University of Münster, 48149 Münster, Germany
| | - Klaus Dreisewerd
- Institute of Hygiene, University of Münster, 48149 Münster, Germany
- Interdisciplinary Center for Clinical Research (IZKF), University of Münster, 48149 Münster, Germany
| | - Jens Soltwisch
- Institute of Hygiene, University of Münster, 48149 Münster, Germany
- Interdisciplinary Center for Clinical Research (IZKF), University of Münster, 48149 Münster, Germany
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19
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Profiling 26,000 Aplysia californica neurons by single cell mass spectrometry reveal neuronal populations with distinct neuropeptide profiles. J Biol Chem 2022; 298:102254. [PMID: 35835221 PMCID: PMC9396074 DOI: 10.1016/j.jbc.2022.102254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/03/2022] [Accepted: 07/07/2022] [Indexed: 11/30/2022] Open
Abstract
Neuropeptides are a chemically diverse class of cell-to-cell signaling molecules that are widely expressed throughout the central nervous system, often in a cell-specific manner. While cell-to-cell differences in neuropeptides is expected, it is often unclear how exactly neuropeptide expression varies among neurons. Here we created a microscopy-guided, high-throughput single cell matrix-assisted laser desorption/ionization mass spectrometry approach to investigate the neuropeptide heterogeneity of individual neurons in the central nervous system of the neurobiological model Aplysia californica, the California sea hare. In all, we analyzed more than 26,000 neurons from 18 animals and assigned 866 peptides from 66 prohormones by mass matching against an in silico peptide library generated from known Aplysia prohormones retrieved from the UniProt database. Louvain–Jaccard (LJ) clustering of mass spectra from individual neurons revealed 40 unique neuronal populations, or LJ clusters, each with a distinct neuropeptide profile. Prohormones and their related peptides were generally found in single cells from ganglia consistent with the prohormones’ previously known ganglion localizations. Several LJ clusters also revealed the cellular colocalization of behaviorally related prohormones, such as an LJ cluster exhibiting achatin and neuropeptide Y, which are involved in feeding, and another cluster characterized by urotensin II, small cardiac peptide, sensorin A, and FRFa, which have shown activity in the feeding network or are present in the feeding musculature. This mass spectrometry–based approach enables the robust categorization of large cell populations based on single cell neuropeptide content and is readily adaptable to the study of a range of animals and tissue types.
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20
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De La Toba EA, Bell SE, Romanova EV, Sweedler JV. Mass Spectrometry Measurements of Neuropeptides: From Identification to Quantitation. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2022; 15:83-106. [PMID: 35324254 DOI: 10.1146/annurev-anchem-061020-022048] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Neuropeptides (NPs), a unique class of neuronal signaling molecules, participate in a variety of physiological processes and diseases. Quantitative measurements of NPs provide valuable information regarding how these molecules are differentially regulated in a multitude of neurological, metabolic, and mental disorders. Mass spectrometry (MS) has evolved to become a powerful technique for measuring trace levels of NPs in complex biological tissues and individual cells using both targeted and exploratory approaches. There are inherent challenges to measuring NPs, including their wide endogenous concentration range, transport and postmortem degradation, complex sample matrices, and statistical processing of MS data required for accurate NP quantitation. This review highlights techniques developed to address these challenges and presents an overview of quantitative MS-based measurement approaches for NPs, including the incorporation of separation methods for high-throughput analysis, MS imaging for spatial measurements, and methods for NP quantitation in single neurons.
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Affiliation(s)
- Eduardo A De La Toba
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois, USA;
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Sara E Bell
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois, USA;
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Elena V Romanova
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois, USA;
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Jonathan V Sweedler
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois, USA;
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
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21
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Tuck M, Grélard F, Blanc L, Desbenoit N. MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives. Front Chem 2022; 10:904688. [PMID: 35615316 PMCID: PMC9124797 DOI: 10.3389/fchem.2022.904688] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/14/2022] [Indexed: 01/22/2023] Open
Abstract
Multimodal imaging is a powerful strategy for combining information from multiple images. It involves several fields in the acquisition, processing and interpretation of images. As multimodal imaging is a vast subject area with various combinations of imaging techniques, it has been extensively reviewed. Here we focus on Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) coupling other imaging modalities in multimodal approaches. While MALDI-MS images convey a substantial amount of chemical information, they are not readily informative about the morphological nature of the tissue. By providing a supplementary modality, MALDI-MS images can be more informative and better reflect the nature of the tissue. In this mini review, we emphasize the analytical and computational strategies to address multimodal MALDI-MSI.
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22
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Bhaduri A, Neumann EK, Kriegstein AR, Sweedler JV. Identification of Lipid Heterogeneity and Diversity in the Developing Human Brain. JACS AU 2021; 1:2261-2270. [PMID: 34977897 PMCID: PMC8717369 DOI: 10.1021/jacsau.1c00393] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Indexed: 06/11/2023]
Abstract
The lipidome is currently understudied but fundamental to life. Within the brain, little is known about cell-type lipid heterogeneity, and even less is known about cell-to-cell lipid diversity because it is difficult to study the lipids within individual cells. Here, we used single-cell mass spectrometry-based protocols to profile the lipidomes of 154 910 single cells across ten individuals consisting of five developmental ages and five brain regions, resulting in a unique lipid atlas available via a web browser of the developing human brain. From these data, we identify differentially expressed lipids across brain structures, cortical areas, and developmental ages. We inferred lipid profiles of several major cell types from this data set and additionally detected putative cell-type specific lipids. This data set will enable further interrogation of the developing human brain lipidome.
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Affiliation(s)
- Aparna Bhaduri
- Department
of Neurology, University of California,
San Francisco, San Francisco, California 94143, United States
- The
Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell
Research, University of California, San
Francisco, San Francisco, California 94143, United States
- Department
of Biological Chemistry, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Elizabeth K. Neumann
- Department
of Chemistry, University of Illinois at
Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Arnold R. Kriegstein
- Department
of Neurology, University of California,
San Francisco, San Francisco, California 94143, United States
- The
Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell
Research, University of California, San
Francisco, San Francisco, California 94143, United States
| | - Jonathan V. Sweedler
- Department
of Chemistry, University of Illinois at
Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Neuroscience
Program, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
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23
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Xu S, Yang C, Yan X, Liu H. Towards high throughput and high information coverage: advanced single-cell mass spectrometric techniques. Anal Bioanal Chem 2021; 414:219-233. [PMID: 34435209 DOI: 10.1007/s00216-021-03624-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/13/2021] [Accepted: 08/17/2021] [Indexed: 12/23/2022]
Abstract
Mass spectrometry (MS) is attractive for single-cell analysis because of its high sensitivity, rich information, and large dynamic ranges, especially for the single-cell metabolome and proteome analysis. Efforts have been made to deal with the throughput and information coverage problems in typical manual single-cell MS techniques. In this review, advanced techniques to improve the automation and throughput for single-cell sampling and single-cell metabolome and proteome MS detection have been discussed. Furthermore, representative MS-based strategies that can increase the in-depth cellular information coverage and achieve the more comprehensive single-cell multiomics information during high throughput detection have been highlighted, providing an ongoing perspective of the MS performance for the single-cell research.
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Affiliation(s)
- Shuting Xu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,Institute of Analytical Food Safety, School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China
| | - Cheng Yang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,Institute of Analytical Food Safety, School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China
| | - Xiuping Yan
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China. .,Institute of Analytical Food Safety, School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.
| | - Huwei Liu
- Institute of Analytical Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.
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24
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Wang Y, Xue P, Cao M, Yu T, Lane ST, Zhao H. Directed Evolution: Methodologies and Applications. Chem Rev 2021; 121:12384-12444. [PMID: 34297541 DOI: 10.1021/acs.chemrev.1c00260] [Citation(s) in RCA: 184] [Impact Index Per Article: 61.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Directed evolution aims to expedite the natural evolution process of biological molecules and systems in a test tube through iterative rounds of gene diversifications and library screening/selection. It has become one of the most powerful and widespread tools for engineering improved or novel functions in proteins, metabolic pathways, and even whole genomes. This review describes the commonly used gene diversification strategies, screening/selection methods, and recently developed continuous evolution strategies for directed evolution. Moreover, we highlight some representative applications of directed evolution in engineering nucleic acids, proteins, pathways, genetic circuits, viruses, and whole cells. Finally, we discuss the challenges and future perspectives in directed evolution.
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Affiliation(s)
- Yajie Wang
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Mingfeng Cao
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Tianhao Yu
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Stephan T Lane
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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25
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Abstract
A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell-cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.
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26
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Choe K, Xue P, Zhao H, Sweedler JV. macroMS: Image-Guided Analysis of Random Objects by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:1180-1188. [PMID: 33822609 PMCID: PMC8102432 DOI: 10.1021/jasms.1c00013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Mass spectrometry imaging is well-suited to characterizing sample surfaces for their chemical content in a spatially resolved manner. However, when the surface contains small objects with significant empty spaces between them, more efficient approaches to sample acquisition are possible. Image-guided mass spectrometry (MS) enables high-throughput analysis of a diverse range of sample types, such as microbial colonies, liquid microdroplets, and others, by recognizing and analyzing selected location targets in an image. Here, we describe an imaging protocol and macroMS, an online software suite that can be used to enhance MS measurements of macroscopic samples that are imaged by a camera or a flatbed scanner. The web-based tool enables users to find and filter targets from the optical images, correct optical distortion issues for improved spatial location of selected targets, input the custom geometry files into an MS device to acquire spectra at the selected locations, and finally, perform limited data analysis and use visualization tools to aid locating samples containing compounds of interest. Using the macroMS suite, an enzyme mutant library of Saccharomyces cerevisiae and nL droplet arrays of Escherichia coli and Pseudomonas fluorescens have been assayed at a rate of ∼2 s/sample.
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Affiliation(s)
- Kisurb Choe
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
- Department of Energy Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Pu Xue
- Department of Energy Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Huimin Zhao
- Department of Energy Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Jonathan V. Sweedler
- Department of Energy Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
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27
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Optical Microscopy-Guided Laser Ablation Electrospray Ionization Ion Mobility Mass Spectrometry: Ambient Single Cell Metabolomics with Increased Confidence in Molecular Identification. Metabolites 2021; 11:metabo11040200. [PMID: 33801673 PMCID: PMC8065410 DOI: 10.3390/metabo11040200] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 12/13/2022] Open
Abstract
Single cell analysis is a field of increasing interest as new tools are continually being developed to understand intercellular differences within large cell populations. Laser-ablation electrospray ionization mass spectrometry (LAESI-MS) is an emerging technique for single cell metabolomics. Over the years, it has been validated that this ionization technique is advantageous for probing the molecular content of individual cells in situ. Here, we report the integration of a microscope into the optical train of the LAESI source to allow for visually informed ambient in situ single cell analysis. Additionally, we have coupled this ‘LAESI microscope’ to a drift-tube ion mobility mass spectrometer to enable separation of isobaric species and allow for the determination of ion collision cross sections in conjunction with accurate mass measurements. This combined information helps provide higher confidence for structural assignment of molecules ablated from single cells. Here, we show that this system enables the analysis of the metabolite content of Allium cepa epidermal cells with high confidence structural identification together with their spatial locations within a tissue.
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28
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Liu R, Yang Z. Single cell metabolomics using mass spectrometry: Techniques and data analysis. Anal Chim Acta 2021; 1143:124-134. [PMID: 33384110 PMCID: PMC7775990 DOI: 10.1016/j.aca.2020.11.020] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/10/2020] [Accepted: 11/17/2020] [Indexed: 02/06/2023]
Abstract
Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cell-to-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future.
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Affiliation(s)
- Renmeng Liu
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA; Alliance Pharma. Inc., Malvern, PA, 19355, USA
| | - Zhibo Yang
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
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29
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Image-guided MALDI mass spectrometry for high-throughput single-organelle characterization. Nat Methods 2021; 18:1233-1238. [PMID: 34594032 PMCID: PMC8490150 DOI: 10.1038/s41592-021-01277-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 08/18/2021] [Indexed: 02/08/2023]
Abstract
Peptidergic dense-core vesicles are involved in packaging and releasing neuropeptides and peptide hormones-critical processes underlying brain, endocrine and exocrine function. Yet, the heterogeneity within these organelles, even for morphologically defined vesicle types, is not well characterized because of their small volumes. We present image-guided, high-throughput mass spectrometry-based protocols to chemically profile large populations of both dense-core vesicles and lucent vesicles for their lipid and peptide contents, allowing observation of the chemical heterogeneity within and between these two vesicle populations. The proteolytic processing products of four prohormones are observed within the dense-core vesicles, and the mass spectral features corresponding to the specific peptide products suggest three distinct dense-core vesicle populations. Notable differences in the lipid mass range are observed between the dense-core and lucent vesicles. These single-organelle mass spectrometry approaches are adaptable to characterize a range of subcellular structures.
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30
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Neumann EK, Djambazova KV, Caprioli RM, Spraggins JM. Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:2401-2415. [PMID: 32886506 PMCID: PMC9278956 DOI: 10.1021/jasms.0c00232] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs.
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Affiliation(s)
- Elizabeth K Neumann
- Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, Tennessee 37205, United States
- Mass Spectrometry Research Center, Vanderbilt University, 465 21st Avenue S #9160, Nashville, Tennessee 37235, United States
| | - Katerina V Djambazova
- Mass Spectrometry Research Center, Vanderbilt University, 465 21st Avenue S #9160, Nashville, Tennessee 37235, United States
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, Tennessee 37235, United States
| | - Richard M Caprioli
- Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, Tennessee 37205, United States
- Mass Spectrometry Research Center, Vanderbilt University, 465 21st Avenue S #9160, Nashville, Tennessee 37235, United States
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, Tennessee 37235, United States
- Department of Pharmacology, Vanderbilt University, 2220 Pierce Avenue, Nashville, Tennessee 37232, United States
- Department of Medicine, Vanderbilt University, 465 21st Avenue S #9160, Nashville, Tennessee 37235, United States
| | - Jeffrey M Spraggins
- Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, Tennessee 37205, United States
- Mass Spectrometry Research Center, Vanderbilt University, 465 21st Avenue S #9160, Nashville, Tennessee 37235, United States
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, Tennessee 37235, United States
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31
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Garrard KP, Ekelöf M, Khodjaniyazova S, Bagley MC, Muddiman DC. A Versatile Platform for Mass Spectrometry Imaging of Arbitrary Spatial Patterns. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:2547-2552. [PMID: 32539373 PMCID: PMC8761386 DOI: 10.1021/jasms.0c00128] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
A vision-system driven platform, RastirX, has been constructed for mass spectrometry imaging (MSI) of arbitrary two-dimensional patterns. The user identifies a region of interest (ROI) by drawing on a live video image of the sample with the computer mouse. Motion commands are automatically generated to move the sample to acquire scan data for the pixels in the ROI. Synchronization of sample stage motion with laser firing and mass spectrometer (MS) scan acquisition is fully automated. RastirX saves a co-registered optical image and the scan location information needed to convert raw MS data into imzML format. Imaging an arbitrarily shaped ROI instead of the minimal enclosing rectangle reduces contamination from off-sample material and significantly reduces acquisition time.
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Affiliation(s)
- Kenneth P. Garrard
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
- Precision Engineering Consortium, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
- Molecular Education, Technology and Research Innovation Center (METRIC), North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Måns Ekelöf
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Sitora Khodjaniyazova
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - M. Caleb Bagley
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - David C. Muddiman
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
- Molecular Education, Technology and Research Innovation Center (METRIC), North Carolina State University, Raleigh, North Carolina 27695, United States
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32
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Clark KD, Philip MC, Tan Y, Sweedler JV. Biphasic Liquid Microjunction Extraction for Profiling Neuronal RNA Modifications by Liquid Chromatography-Tandem Mass Spectrometry. Anal Chem 2020; 92:12647-12655. [PMID: 32786436 PMCID: PMC7496823 DOI: 10.1021/acs.analchem.0c02830] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
RNA modifications are emerging as critical players in the spatiotemporal regulation of gene expression. Although liquid chromatography-tandem mass spectrometry (LC-MS/MS) enables the simultaneous quantification of numerous enzymatically modified RNAs in a biological sample, conventional RNA extraction and enzymatic digestion protocols that are employed prior to analysis have precluded the application of this technique for small-volume samples. In this study, a biphasic liquid microjunction (LMJ) extraction system using coaxial capillaries that direct and aspirate extraction solvents onto a ∼350 μm diameter sample spot was developed and applied for the extraction of RNA from individual cell clusters in the central nervous system of the marine mollusk Aplysia californica. To maximize RNA recoveries, optimized extraction solvents consisting of 10% methanol and chloroform were evaluated under dynamic and static extraction conditions. An MS-compatible RNA digestion buffer was developed to minimize the number of sample-transfer steps and facilitate the direct enzymatic digestion of extracted RNA within the sample collection tube. Compared to RNA extraction using a conventional phenol-chloroform method, the LMJ-based method provided a 3-fold greater coverage of the neuronal epitranscriptome for similar amounts of tissues and also produced mRNA of sufficient purity for reverse transcription polymerase chain reaction amplification. Using this approach, the expression of RNA-modifying enzymes in a given neuronal cell cluster can be characterized and simultaneously correlated with the LC-MS/MS analysis of RNA modifications within the same subset of neurons.
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Affiliation(s)
- Kevin D. Clark
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Marina C. Philip
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Yanqi Tan
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Jonathan V. Sweedler
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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33
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Xie YR, Castro DC, Bell SE, Rubakhin SS, Sweedler JV. Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning. Anal Chem 2020; 92:9338-9347. [PMID: 32519839 PMCID: PMC7374983 DOI: 10.1021/acs.analchem.0c01660] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity, and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, cell function, and phenotype are not always understood. Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry have enabled the high-throughput, multiplexed chemical analysis of single cells, capable of resolving hundreds of molecules in each mass spectrum. We developed a machine learning workflow to classify single cells according to their mass spectra based on cell groups of interest (GOI), e.g., neurons vs astrocytes. Three data sets from various cell groups were acquired on three different mass spectrometer platforms representing thousands of individual cell spectra that were collected and used to validate the single cell classification workflow. The trained models achieved >80% classification accuracy and were subjected to the recently developed instance-based model interpretation framework, SHapley Additive exPlanations (SHAP), which locally assigns feature importance for each single-cell spectrum. SHAP values were used for both local and global interpretations of our data sets, preserving the chemical heterogeneity uncovered by the single-cell analysis while offering the ability to perform supervised analysis. The top contributing mass features to each of the GOI were ranked and selected using mean absolute SHAP values, highlighting the features that are specific to the defined GOI. Our approach provides insight into discriminating the chemical profiles of the single cells through interpretable machine learning, facilitating downstream analysis and validation.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Sara E. Bell
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Stanislav S. Rubakhin
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Jonathan V. Sweedler
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
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34
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Optically guided mass spectrometry to screen microbial colonies for directed enzyme evolution. Methods Enzymol 2020. [PMID: 32943148 DOI: 10.1016/bs.mie.2020.04.054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Directed evolution is a well-established and powerful tool for enzyme engineering, which consists of iterative rounds of creating and screening a library of variants. In many cases, the ability to characterize these variants in high-throughput remains a bottleneck. In addition, profiling of desired candidates becomes even more challenging when engineering multiple enzymes in a biochemical pathway. In this chapter, we describe a label-free, high-throughput method for the engineering of multistep enzymatic reactions in bacterial colonies via optically guided matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) mass spectrometry (MS). This method is able to detect products, reactants, and byproducts with high sensitivity and accuracy. We demonstrate its effectiveness in two applications related to natural product biosynthesis, including facile creation of analog of the peptidic antibiotic plantazolicin and rapid profiling of congeners of rhamnolipid. Computational algorithms were developed to process and visualize the resulting mass spectral data sets. In both cases, improved MS acquisition efficiency and information-rich insights were obtained through this technique on large populations of colonies at a rate of 1-2.5s per colony. This method should be generally applicable to high-throughput phenotyping of microbial libraries from a wide range of enzymatic reactions.
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35
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Spraker JE, Luu GT, Sanchez LM. Imaging mass spectrometry for natural products discovery: a review of ionization methods. Nat Prod Rep 2020; 37:150-162. [PMID: 31364647 PMCID: PMC6992513 DOI: 10.1039/c9np00038k] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Covering: 2009-2019 Over the last decade, methods in imaging mass spectrometry (IMS) have progressively improved and diversified toward a variety of applications in natural products research. Because IMS allows for the spatial mapping of the production and distribution of biologically active molecules in situ, it facilitates phenotype and organelle driven discovery efforts. As practitioners of IMS for natural products discovery, we find one of the most important aspects of these experiments is the sample preparation and compatibility with different ionization sources that are available to a given researcher. As such, we have focused this mini review to cover types of ionization sources that have been used in natural products discovery applications and provided concrete examples of use for natural products discovery while discussing the advantages and limitations of each method. We aim for this article to serve as a resource to guide the broader natural product community interested in IMS toward the application/method that would best serve their natural product discovery needs given the sample and analyte(s) of interest. This mini review has been limited to applications using natural products and thus is not exhaustive of all possible ionization methods which have only been applied to image other types of samples such as mammalian tissues. Additionally, we briefly review how IMS has been coupled with other imaging platforms, such as microscopy, to enhance information outputs as well as offer our future perspectives on the incorporation of IMS in natural products discovery.
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Affiliation(s)
- Joseph E Spraker
- Hexagon Bio, 1505 Adams Drive, Suite A, Menlo Park, CA 94025, USA
| | - Gordon T Luu
- Department of Pharmaceutical Sciences, University of Illinois at Chicago, 833 S Wood St, Chicago, IL 60612, USA,
| | - Laura M Sanchez
- Department of Pharmaceutical Sciences, University of Illinois at Chicago, 833 S Wood St, Chicago, IL 60612, USA,
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Fu L, Zhang J, Si T. Recent advances in high-throughput mass spectrometry that accelerates enzyme engineering for biofuel research. ACTA ACUST UNITED AC 2020. [DOI: 10.1186/s42500-020-0011-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
AbstractEnzymes play indispensable roles in producing biofuels, a sustainable and renewable source of transportation fuels. Lacking rational design rules, the development of industrially relevant enzyme catalysts relies heavily on high-throughput screening. However, few universal methods exist to rapidly characterize large-scale enzyme libraries. Therefore, assay development is necessary on an ad hoc basis to link enzyme properties to spectrophotometric signals and often requires the use of surrogate, optically active substrates. On the other hand, mass spectrometry (MS) performs label-free enzyme assays that utilize native substrates and is therefore generally applicable. But the analytical speed of MS is considered rate limiting, mainly due to the use of time-consuming chromatographic separation in traditional MS analysis. Thanks to new instrumentation and sample preparation methods, direct analyte introduction into a mass spectrometer without a prior chromatographic step can be achieved by laser, microfluidics, and acoustics, so that each sample can be analyzed within seconds. Here we review recent advances in MS platforms that improve the throughput of enzyme library screening and discuss how these advances can potentially facilitate biofuel research by providing high sensitivity, selectivity and quantitation that are difficult to obtain using traditional assays. We also highlight the limitations of current MS assays in studying biofuel-related enzymes and propose possible solutions.
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Neumann EK, Do TD, Comi TJ, Sweedler JV. Exploring the Fundamental Structures of Life: Non-Targeted, Chemical Analysis of Single Cells and Subcellular Structures. Angew Chem Int Ed Engl 2019; 58:9348-9364. [PMID: 30500998 PMCID: PMC6542728 DOI: 10.1002/anie.201811951] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Indexed: 01/14/2023]
Abstract
Cells are a basic functional and structural unit of living organisms. Both unicellular communities and multicellular species produce an astonishing chemical diversity, enabling a wide range of divergent functions, yet each cell shares numerous aspects that are common to all living organisms. While there are many approaches for studying this chemical diversity, only a few are non-targeted and capable of analyzing hundreds of different chemicals at cellular resolution. Here, we review the non-targeted approaches used to perform comprehensive chemical analyses, provide chemical imaging information, or obtain high-throughput single-cell profiling data. Single-cell measurement capabilities are rapidly increasing in terms of throughput, limits of detection, and completeness of the chemical analyses; these improvements enable their application to understand ever more complex physiological phenomena, such as learning, memory, and behavior.
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Affiliation(s)
- Elizabeth K. Neumann
- Department of Chemistry and the Beckman Institute for Advanced Science and Technology, 405 N. Mathews Avenue, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Thanh D. Do
- Department of Chemistry, 1420 Circle Drive, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Troy J. Comi
- Department of Chemistry and the Beckman Institute for Advanced Science and Technology, 405 N. Mathews Avenue, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Jonathan V. Sweedler
- Department of Chemistry and the Beckman Institute for Advanced Science and Technology, 405 N. Mathews Avenue, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
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38
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Neumann EK, Ellis JF, Triplett AE, Rubakhin SS, Sweedler JV. Lipid Analysis of 30 000 Individual Rodent Cerebellar Cells Using High-Resolution Mass Spectrometry. Anal Chem 2019; 91:7871-7878. [PMID: 31122012 DOI: 10.1021/acs.analchem.9b01689] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Single-cell measurements aid our understanding of chemically heterogeneous systems such as the brain. Lipids are one of the least studied chemical classes, and their cell-to-cell heterogeneity remains largely unexplored. We adapted microscopy-guided single-cell profiling using matrix-assisted laser desorption/ionization ion cyclotron resonance mass spectrometry to profile the lipid composition of over 30 000 individual rat cerebellar cells. We detected 520 lipid features, many of which were found in subsets of cells; Louvain clustering identified 101 distinct groups that can be correlated to neuronal and astrocytic classifications and lipid classes. Overall, the two most common lipids found were [PC(32:0)+H]+ and [PC(34:1)+H]+, which were present within 98.9 and 89.5% of cells, respectively; lipid signals present in <1% of cells were also detected, including [PC(34:1)+K]+ and [PG(40:2(OH))+Na]+. These results illustrate the vast lipid heterogeneity found within rodent cerebellar cells and hint at the distinct functional consequences of this heterogeneity.
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Yin L, Zhang Z, Liu Y, Gao Y, Gu J. Recent advances in single-cell analysis by mass spectrometry. Analyst 2019; 144:824-845. [PMID: 30334031 DOI: 10.1039/c8an01190g] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Cells are the most basic structural units that play vital roles in the functioning of living organisms. Analysis of the chemical composition and content of a single cell plays a vital role in ensuring precise investigations of cellular metabolism, and is a crucial aspect of lipidomic and proteomic studies. In addition, structural knowledge provides a better understanding of cell behavior as well as the cellular and subcellular mechanisms. However, single-cell analysis can be very challenging due to the very small size of each cell as well as the large variety and extremely low concentrations of substances found in individual cells. On account of its high sensitivity and selectivity, mass spectrometry holds great promise as an effective technique for single-cell analysis. Numerous mass spectrometric techniques have been developed to elucidate the molecular profiles at the cellular level, including electrospray ionization mass spectrometry (ESI-MS), secondary ion mass spectrometry (SIMS), laser-based mass spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). In this review, the recent advances in single-cell analysis by mass spectrometry are summarized. The strategies of different ionization modes to achieve single-cell analysis are classified and discussed in detail.
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Affiliation(s)
- Lei Yin
- Research Institute of Translational Medicine, The First Hospital of Jilin University, Jilin University, Dongminzhu Street, Changchun 130061, PR China.
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Neumann EK, Comi TJ, Rubakhin SS, Sweedler JV. Lipid Heterogeneity between Astrocytes and Neurons Revealed by Single‐Cell MALDI‐MS Combined with Immunocytochemical Classification. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201812892] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Elizabeth K. Neumann
- Department of Chemistry and Beckman Institute for Advanced Science and technologyUniversity of Illinois at Urbana-Champaign 405 N. Matthews Ave. Urbana IL 61801 USA
| | - Troy J. Comi
- Department of Chemistry and Beckman Institute for Advanced Science and technologyUniversity of Illinois at Urbana-Champaign 405 N. Matthews Ave. Urbana IL 61801 USA
| | - Stanislav S. Rubakhin
- Department of Chemistry and Beckman Institute for Advanced Science and technologyUniversity of Illinois at Urbana-Champaign 405 N. Matthews Ave. Urbana IL 61801 USA
| | - Jonathan V. Sweedler
- Department of Chemistry and Beckman Institute for Advanced Science and technologyUniversity of Illinois at Urbana-Champaign 405 N. Matthews Ave. Urbana IL 61801 USA
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Neumann EK, Do TD, Comi TJ, Sweedler JV. Erforschung der fundamentalen Strukturen des Lebens: Nicht zielgerichtete chemische Analyse von Einzelzellen und subzellulären Strukturen. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201811951] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Elizabeth K. Neumann
- Department of Chemistry and the Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-Champaign 405 N. Mathews Avenue Urbana IL 61801 USA
| | - Thanh D. Do
- Department of ChemistryUniversity of Tennessee 1420 Circle Drive Knoxville TN 37996 USA
| | - Troy J. Comi
- Department of Chemistry and the Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-Champaign 405 N. Mathews Avenue Urbana IL 61801 USA
| | - Jonathan V. Sweedler
- Department of Chemistry and the Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-Champaign 405 N. Mathews Avenue Urbana IL 61801 USA
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Neumann EK, Comi TJ, Rubakhin SS, Sweedler JV. Lipid Heterogeneity between Astrocytes and Neurons Revealed by Single-Cell MALDI-MS Combined with Immunocytochemical Classification. Angew Chem Int Ed Engl 2019; 58:5910-5914. [PMID: 30811763 DOI: 10.1002/anie.201812892] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 01/18/2019] [Indexed: 12/15/2022]
Abstract
Transcriptomics characterizes cells based on their potential molecular repertoire whereas direct mass spectrometry (MS) provides information on the actual compounds present within cells. Single-cell matrix-assisted laser desorption/ionization (MALDI) MS can measure the chemical contents of individual cells but spectra are difficult to correlate to conventional cell types, limiting the metabolic information obtained. We present a protocol that combines MALDI-MS with immunocytochemistry to assay over a thousand individual rat brain cells. The approach entwines the wealth of knowledge obtained by immunocytochemical profiling with mass spectral information on the predominant lipids within each cell. While many lipid species showed a high degree of similarity between neurons and astrocytes, seventeen significantly differed between the two cell types, including four phosphatidylethanolamines elevated in astrocytes and nine phosphatidylcholines in neurons.
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Affiliation(s)
- Elizabeth K Neumann
- Department of Chemistry and Beckman Institute for Advanced Science and technology, University of Illinois at Urbana-Champaign, 405 N. Matthews Ave., Urbana, IL, 61801, USA
| | - Troy J Comi
- Department of Chemistry and Beckman Institute for Advanced Science and technology, University of Illinois at Urbana-Champaign, 405 N. Matthews Ave., Urbana, IL, 61801, USA
| | - Stanislav S Rubakhin
- Department of Chemistry and Beckman Institute for Advanced Science and technology, University of Illinois at Urbana-Champaign, 405 N. Matthews Ave., Urbana, IL, 61801, USA
| | - Jonathan V Sweedler
- Department of Chemistry and Beckman Institute for Advanced Science and technology, University of Illinois at Urbana-Champaign, 405 N. Matthews Ave., Urbana, IL, 61801, USA
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Schmitt ND, Rawlins CM, Randall EC, Wang X, Koller A, Auclair JR, Kowalski JM, Kowalski PJ, Luther E, Ivanov AR, Agar NY, Agar JN. Genetically Encoded Fluorescent Proteins Enable High-Throughput Assignment of Cell Cohorts Directly from MALDI-MS Images. Anal Chem 2019; 91:3810-3817. [PMID: 30839199 PMCID: PMC6827431 DOI: 10.1021/acs.analchem.8b03454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) provides a unique in situ chemical profile that can include drugs, nucleic acids, metabolites, lipids, and proteins. MSI of individual cells (of a known cell type) affords a unique insight into normal and disease-related processes and is a prerequisite for combining the results of MSI and other single-cell modalities (e.g. mass cytometry and next-generation sequencing). Technological barriers have prevented the high-throughput assignment of MSI spectra from solid tissue preparations to their cell type. These barriers include obtaining a suitable cell-identifying image (e.g. immunohistochemistry) and obtaining sufficiently accurate registration of the cell-identifying and MALDI-MS images. This study introduces a technique that overcame these barriers by assigning cell type directly from mass spectra. We hypothesized that, in MSI from mice with a defined fluorescent protein expression pattern, the fluorescent protein's molecular ion could be used to identify cell cohorts. A method was developed for the purification of enhanced yellow fluorescent protein (EYFP) from mice. To determine EYFP's molecular mass for MSI studies, we performed intact mass analysis and characterized the protein's primary structure and post-translational modifications through various techniques. MALDI-MSI methods were developed to enhance the detection of EYFP in situ, and by extraction of EYFP's molecular ion from MALDI-MS images, automated, whole-image assignment of cell cohorts was achieved. This method was validated using a well-characterized mouse line that expresses EYFP in motor and sensory neurons and should be applicable to hundreds of commercially available mice (and other animal) strains comprising a multitude of cell-specific fluorescent labels.
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Affiliation(s)
- Nicholas D. Schmitt
- Department of Chemistry and Chemical Biology, and Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, 02115, USA
- These authors contributed equally to this work
| | - Catherine M. Rawlins
- Department of Chemistry and Chemical Biology, and Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, 02115, USA
- These authors contributed equally to this work
| | - Elizabeth C. Randall
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Xianzhe Wang
- Department of Chemistry and Chemical Biology, and Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, 02115, USA
| | - Antonius Koller
- Department of Chemistry and Chemical Biology, and Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, 02115, USA
| | - Jared R. Auclair
- Department of Chemistry and Chemical Biology, and Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, 02115, USA
- Biopharmaceutical Analysis Training Laboratory (BATL), Northeastern University Innovation Campus, Burlington, MA, 01803, USA
| | | | | | - Ed Luther
- Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, 02115, USA
| | - Alexander R. Ivanov
- Department of Chemistry and Chemical Biology, and Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, 02115, USA
| | - Nathalie Y.R. Agar
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Jeffrey N. Agar
- Department of Chemistry and Chemical Biology, and Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, 02115, USA
- Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, 02115, USA
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44
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Neumann EK, Comi TJ, Spegazzini N, Mitchell JW, Rubakhin SS, Gillette MU, Bhargava R, Sweedler JV. Multimodal Chemical Analysis of the Brain by High Mass Resolution Mass Spectrometry and Infrared Spectroscopic Imaging. Anal Chem 2018; 90:11572-11580. [PMID: 30188687 DOI: 10.1021/acs.analchem.8b02913] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The brain functions through chemical interactions between many different cell types, including neurons and glia. Acquiring comprehensive information on complex, heterogeneous systems requires multiple analytical tools, each of which have unique chemical specificity and spatial resolution. Multimodal imaging generates complementary chemical information via spatially localized molecular maps, ideally from the same sample, but requires method enhancements that span from data acquisition to interpretation. We devised a protocol for performing matrix-assisted laser desorption/ionization (MALDI)-Fourier transform ion cyclotron resonance-mass spectrometry imaging (MSI), followed by infrared (IR) spectroscopic imaging on the same specimen. Multimodal measurements from the same tissue provide precise spatial alignment between modalities, enabling more advanced image processing such as image fusion and sharpening. Performing MSI first produces higher quality data from each technique compared to performing IR imaging before MSI. The difference is likely due to fixing the tissue section during MALDI matrix removal, thereby preventing analyte degradation occurring during IR imaging from an unfixed specimen. Leveraging the unique capabilities of each modality, we utilized pan sharpening of MS (mass spectrometry) ion images with selected bands from IR spectroscopy and midlevel data fusion. In comparison to sharpening with histological images, pan sharpening can employ a plethora of IR bands, producing sharpened MS images while retaining the fidelity of the initial ion images. Using Laplacian pyramid sharpening, we determine the localization of several lipids present within the hippocampus with high mass accuracy at 5 μm pixel widths. Further, through midlevel data fusion of the imaging data sets combined with k-means clustering, the combined data set discriminates between additional anatomical structures unrecognized by the individual imaging approaches. Significant differences between molecular ion abundances are detected between relevant structures within the hippocampus, such as the CA1 and CA3 regions. Our methodology provides high quality multiplex and multimodal chemical imaging of the same tissue sample, enabling more advanced data processing and analysis routines.
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Tillmaand EG, Sweedler JV. Integrating Mass Spectrometry with Microphysiological Systems for Improved Neurochemical Studies. ACTA ACUST UNITED AC 2018; 2. [PMID: 30148282 DOI: 10.21037/mps.2018.05.01] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Microphysiological systems, often referred to as "organs-on-chips", are in vitro platforms designed to model the spatial, chemical, structural, and physiological elements of in vivo cellular environments. They enhance the evaluation of complex engineered biological systems and are a step between traditional cell culture and in vivo experimentation. As neurochemists and measurement scientists studying the molecules involved in intercellular communication in the nervous system, we focus here on recent advances in neuroscience using microneurological systems and their potential to interface with mass spectrometry. We discuss a number of examples - microfluidic devices, spheroid cultures, hydrogels, scaffolds, and fibers - highlighting those that would benefit from mass spectrometric technologies to obtain improved chemical information.
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Affiliation(s)
- Emily G Tillmaand
- Department of Chemistry, the Neuroscience Program and the Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Jonathan V Sweedler
- Department of Chemistry, the Neuroscience Program and the Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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46
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Do TD, Ellis JF, Neumann EK, Comi TJ, Tillmaand EG, Lenhart AE, Rubakhin SS, Sweedler JV. Optically Guided Single Cell Mass Spectrometry of Rat Dorsal Root Ganglia to Profile Lipids, Peptides and Proteins. Chemphyschem 2018; 19:1180-1191. [PMID: 29544029 PMCID: PMC5980748 DOI: 10.1002/cphc.201701364] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Indexed: 12/16/2022]
Abstract
The mammalian dorsal root ganglia (DRG) are located on the dorsal roots of the spinal nerves and contain cell bodies of primary sensory neurons. DRG cells have been classified into subpopulations based on their size, morphology, intracellular markers, response to stimuli, and neuropeptides. To understand the connections between DRG chemical heterogeneity and cellular function, we performed optically guided, high-throughput single cell profiling using sequential matrix-assisted laser desorption/ionization mass spectrometry (MS) to detect lipids, peptides, and several proteins in individual DRG cells. Statistical analysis of the resulting mass spectra allows stratification of the DRG population according to cellular morphology and, presumably, major cell types. A subpopulation of small cells contained myelin proteins, which are abundant in Schwann cells, and mass spectra of several larger cells contained peaks matching neurofilament, vimentin, myelin basic protein S, and thymosin beta proteins. Of the over 1000 cells analyzed, approximately 78 % produced putative peptide-rich spectra, allowing the population to be classified into three distinct cell types. Two signals with m/z 4404 and 5487 were exclusively observed in a cell type, but could not be matched to results of our previous liquid chromatography-MS analyses.
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Affiliation(s)
- Thanh D. Do
- Department of Chemistry and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, United States
| | - Joseph F. Ellis
- Department of Chemistry and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, United States
| | - Elizabeth K. Neumann
- Department of Chemistry and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, United States
| | - Troy J. Comi
- Department of Chemistry and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, United States
| | - Emily G. Tillmaand
- Department of Chemistry and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, United States
| | - Ashley E. Lenhart
- Department of Chemistry and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, United States
| | - Stanislav S. Rubakhin
- Department of Chemistry and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, United States
| | - Jonathan V. Sweedler
- Department of Chemistry and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, United States
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Abstract
Metabolomics, the characterization of metabolites and their changes within biological systems, has seen great technological and methodological progress over the past decade. Most metabolomic experiments involve the characterization of the small-molecule content of fluids or tissue homogenates. While these microliter and larger volume metabolomic measurements can characterize hundreds to thousands of compounds, the coverage of molecular content decreases as sample sizes are reduced to the nanoliter and even to the picoliter volume range. Recent progress has enabled the ability to characterize the major molecules found within specific individual cells. Especially within the brain, a myriad of cell types are colocalized, and oftentimes only a subset of these cells undergo changes in both healthy and pathological states. Here we highlight recent progress in mass spectrometry-based approaches used for single cell metabolomics, emphasizing their application to neuroscience research. Single cell studies can be directed to measuring differences between members of populations of similar cells (e.g., oligodendrocytes), as well as characterizing differences between cell types (e.g., neurons and astrocytes), and are especially useful for measuring changes occurring during different behavior states, exposure to diets and drugs, neuronal activity, and disease. When combined with other omics approaches such as transcriptomics, and with morphological and physiological measurements, single cell metabolomics aids fundamental neurochemical studies, has great potential in pharmaceutical development, and should improve the diagnosis and treatment of brain diseases.
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Affiliation(s)
- Meng Qi
- Department of Chemistry and the Beckman Institute, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
| | - Marina C Philip
- Department of Chemistry and the Beckman Institute, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
| | - Ning Yang
- Department of Chemistry and the Beckman Institute, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
| | - Jonathan V Sweedler
- Department of Chemistry and the Beckman Institute, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
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Si T, Li B, Comi TJ, Wu Y, Hu P, Wu Y, Min Y, Mitchell DA, Zhao H, Sweedler JV. Profiling of Microbial Colonies for High-Throughput Engineering of Multistep Enzymatic Reactions via Optically Guided Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry. J Am Chem Soc 2017; 139:12466-12473. [PMID: 28792758 PMCID: PMC5600186 DOI: 10.1021/jacs.7b04641] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) mass spectrometry (MS) imaging has been used for rapid phenotyping of enzymatic activities, but is mainly limited to single-step conversions. Herein we report a label-free method for high-throughput engineering of multistep biochemical reactions based on optically guided MALDI-ToF MS analysis of bacterial colonies. The bacterial cells provide containment of multiple enzymes and access to substrates and cofactors via metabolism. Automated MALDI-ToF MS acquisition from randomly distributed colonies simplifies procedures to prepare strain libraries without liquid handling. MALDI-ToF MS profiling was utilized to screen both substrate and enzyme libraries for natural product biosynthesis. Computational algorithms were developed to process and visualize the resulting mass spectral data sets. For analogues of the peptidic antibiotic plantazolicin, multivariate analyses by t-distributed stochastic neighbor embedding were used to group similar spectra for rapid identification of nonisobaric variants. After MALDI-ToF MS screening, follow-up analyses using high-resolution MS and tandem MS were readily performed on the same sample target. Separately, relative ion intensities of rhamnolipid congeners with various lipid moieties were evaluated to engineer enzymatic specificity. The glycolipid profiles of each colony were overlaid with optical images to facilitate the recovery of desirable mutants. For both the antibiotic and rhamnolipid cases, large populations of colonies were rapidly surveyed at the molecular level, providing information-rich insights not easily obtained with traditional screening assays. Utilizing standard microbiological techniques with routine microscopy and MALDI-ToF MS instruments, this simple yet effective workflow is applicable for a wide range of screening campaigns targeting multistep enzymatic reactions.
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Affiliation(s)
| | - Bin Li
- State Key Laboratory of Natural Medicines, China Pharmaceutical University , Nanjing 210009, China
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Comi TJ, Makurath MA, Philip MC, Rubakhin SS, Sweedler JV. MALDI MS Guided Liquid Microjunction Extraction for Capillary Electrophoresis-Electrospray Ionization MS Analysis of Single Pancreatic Islet Cells. Anal Chem 2017; 89:7765-7772. [PMID: 28636327 PMCID: PMC5518278 DOI: 10.1021/acs.analchem.7b01782] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 06/21/2017] [Indexed: 12/14/2022]
Abstract
The ability to characterize chemical heterogeneity in biological structures is essential to understanding cellular-level function in both healthy and diseased states, but these variations remain difficult to assess using a single analytical technique. While mass spectrometry (MS) provides sufficient sensitivity to measure many analytes from volume-limited samples, each type of mass spectrometric analysis uncovers only a portion of the complete chemical profile of a single cell. Matrix-assisted laser desorption/ionization (MALDI) MS and capillary electrophoresis electrospray ionization (CE-ESI)-MS are complementary analytical platforms frequently utilized for single-cell analysis. Optically guided MALDI MS provides a high-throughput assessment of lipid and peptide content for large populations of cells, but is typically nonquantitative and fails to detect many low-mass metabolites because of MALDI matrix interferences. CE-ESI-MS allows quantitative measurements of cellular metabolites and increased analyte coverage, but has lower throughput because the electrophoretic separation is relatively slow. In this work, the figures of merit for each technique are combined via an off-line method that interfaces the two MS systems with a custom liquid microjunction surface sampling probe. The probe is mounted on an xyz translational stage, providing 90.6 ± 0.6% analyte removal efficiency with a spatial targeting accuracy of 42.8 ± 2.3 μm. The analyte extraction footprint is an elliptical area with a major diameter of 422 ± 21 μm and minor diameter of 335 ± 27 μm. To validate the approach, single rat pancreatic islet cells were rapidly analyzed with optically guided MALDI MS to classify each cell into established cell types by their peptide content. After MALDI MS analysis, a majority of the analyte remains for follow-up measurements to extend the overall chemical coverage. Optically guided MALDI MS was used to identify individual pancreatic islet α and β cells, which were then targeted for liquid microjunction extraction. Extracts from single α and β cells were analyzed with CE-ESI-MS to obtain qualitative information on metabolites, including amino acids. Matching the molecular masses and relative migration times of the extracted analytes and related standards allowed identification of several amino acids. Interestingly, dopamine was consistently detected in both cell types. The results demonstrate the successful interface of optical microscopy-guided MALDI MS and CE-ESI-MS for sequential chemical profiling of individual, mammalian cells.
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Affiliation(s)
- Troy J. Comi
- Department
of Chemistry and the Beckman Institute, and Department of Molecular and Integrative
Physiology, University of Illinois, Urbana−Champaign, Illinois 61801, United
States
| | - Monika A. Makurath
- Department
of Chemistry and the Beckman Institute, and Department of Molecular and Integrative
Physiology, University of Illinois, Urbana−Champaign, Illinois 61801, United
States
| | - Marina C. Philip
- Department
of Chemistry and the Beckman Institute, and Department of Molecular and Integrative
Physiology, University of Illinois, Urbana−Champaign, Illinois 61801, United
States
| | - Stanislav S. Rubakhin
- Department
of Chemistry and the Beckman Institute, and Department of Molecular and Integrative
Physiology, University of Illinois, Urbana−Champaign, Illinois 61801, United
States
| | - Jonathan V. Sweedler
- Department
of Chemistry and the Beckman Institute, and Department of Molecular and Integrative
Physiology, University of Illinois, Urbana−Champaign, Illinois 61801, United
States
| |
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